Large Language Models (LLMs)

Example LLMs: ChatGPT, Gemini, Llamma, Claude, capable of understanding and generating human-like language, images, music.

Data scope: Trained on massive datasets, including PetaBytes of internet text, Wikipedia and Pubmed.

Applications: LLMs are utilized in chatbots, text generation, reasoning and problem solving, creative output.

Scalability: LLMs are based on Generative Pre-trained Transformers (GPTs), can be "prompt-engineered" for complex tasks.

Definition: Artificial Neural Networks (ANNs), are fundamentally complex non-linear function estimators - pattern classifiers.

Innovation: GPTs are ANNs that implement "multi-head attention", enabling capture of long-range patterns in training data, emergent "intelligence".

Text Generation: LLMs create responses by predicting likely sequences of words, based on billions of probabilities.

Inference Techniques: The models use sophisticated algorithms to generate text that aligns with context and user input.

Diversity: Can produce a wide range of responses, from factual information to hallucinations and "deepfakes".

Fine-Tuning: LLMs can be fine-tuned with data from specific domains, enhancing their relevance and performance.

Task-Specific: Fine-tuning produces tailored AI models for specialized applications, i.e. bioinformatics / biomedical research.

Alternative: Fine-tuning costs computing time, instead similar achievements via carefully designed prompt-engineering.

Clarification: OpenAI (the company behind ChatGPT) offers rich functionality through their API

Assistants: user file search and code Interpreter, external API function calling by the AI

Functionality: build custom AI applications around user’s data